Developers have significantly improved hardware and software solutions for digital image editing and manipulation. Indeed, conventional digital image editing systems can generate digital images that reflect interactions between objects portrayed in a digital image and modifications to their surrounding environments. For example, some conventional digital image editing systems can generate digital images that reflect additional or modified light sources illuminating an environment. To illustrate, some conventional image editing systems generate models of object geometry, physical material properties, and lighting and then create digital images by modeling light interaction based on these properties. Moreover, some conventional image editing systems capture hundreds of digital images of an object with different light sources and then selectively combine these digital images to generate new digital images with different lighting configurations.
Although these conventional systems can generate digital images with modified light, they have several technological shortcomings in relation to accuracy, efficiency, and flexibility. For example, conventional image editing systems are typically inaccurate in producing images of objects realistically interacting with alternate lighting conditions. To illustrate, due to the complexity of modeling object geometry and material properties, conventional image editing systems often make numerous assumptions in order to come to a solution. Consequently, these models fail to generate digital images that accurately reflect objects in new lighting conditions. Similarly, conventional systems that combine existing digital images often fail to have sufficient source images to accurately portray differently lighting conditions. Accordingly, conventional systems often fail to generate digital images that accurately portray complex objects under novel lighting and/or sophisticated lighting effects, such as specular highlights, shadows, or inter-reflections.
In addition to accuracy concerns, conventional image relighting systems are also inefficient. For example, as mentioned above, conventional systems that combine digital images often require hundreds or thousands of source images of an object illuminated by different lighting conditions. Acquiring, storing, and processing such high-volume digital images places exorbitant storage and processing demands on computer systems. Similarly, conventional systems that attempt to directly model object geometry, material properties, and lighting require significant computing resources and time to generate resulting digital images.
In addition to problems with accuracy and efficiency, conventional systems are also inflexible. Indeed, conventional systems that combine hundreds of digital images require users to rigidly capture hundreds of digital images of an object. This rigidity makes such systems impractical to utilize in most circumstances. Similarly, conventional systems that model physical properties operate in conjunction with limited types of objects, such as simple animations, but cannot flexibly apply to a wide range of complex (e.g., real-world) objects.